Jardim et al. (2024): Supporting Information

Appendix S1

This document contains all data analyses presented in the main text as well as supporting information mentioned and extra information on the analysis for those interested (not cross-referenced).

The analysis was organized using the make-line pipeline tool provided by targets (Landau 2021). You will find raw data as well as all functions used in the pipeline in the project’s research compendium created with the rcompendium package (Casajus N. 2022).

You can take a look at the pipeline in order to have an idea of the different steps for creating the main models and figures. Each target is defined at the _targets.R file in the research compendium.

 

 

 

Analysis pipeline. Total runtime and storage size of each object (target) are shown.

 

Table of figures

Supporting	Main text
Figure S 1	Figure 1
Figure S 2	Figure 2
Figure S 3	Figure 3
Figure S 4
Figure S 5
Figure S 6
Figure S 7
Figure S 9
Figure S 8
Figure S 10

Table of tables

Unfortunately links only redirect to tabsets open by default but not hidden ones. Please look for tables related to analyses by phylum or by compartment in their respective tabsets.

Supporting Tables
Table S 1
Table S 2
Table S 3
Table S 4
Table S 5
Table S 6
Table S 7
Table S 8
Table S 9
Table S 10
Table S 11
Table S 12
Table S 13
Table S 14

Packages

library(dplyr)
library(tidyr)
library(vegan)
library(ggplot2)
library(targets)
library(ggrepel)
library(patchwork)
library(plotly)
library(htmlwidgets)
library(png)
theme_set(theme_light())

Macrofauna data availabity

tar_load(faunadata)
knitr::kable(head(faunadata))

 

First lines of the original macrofaunal data

Point	Site	Replicate	Method	Habitat	Year	Season	Date	Species	Abundance
Belle-ile 1	Belle-ile	1	Grab	Maerl	2007	Spring	2007-03-11 00:00:00	Abludomelita gladiosa	1
Belle-ile 1	Belle-ile	1	Grab	Maerl	2007	Spring	2007-03-11 00:00:00	Abra alba	1
Belle-ile 1	Belle-ile	1	Grab	Maerl	2007	Spring	2007-03-11 00:00:00	Ampharete spp.	1
Belle-ile 1	Belle-ile	1	Grab	Maerl	2007	Spring	2007-03-11 00:00:00	Amphipholis squamata	2
Belle-ile 1	Belle-ile	1	Grab	Maerl	2007	Spring	2007-03-11 00:00:00	Anapagurus hyndmanni	1
Belle-ile 1	Belle-ile	1	Grab	Maerl	2007	Spring	2007-03-11 00:00:00	Aonides oxycephala	3

 

samplespoint <- faunadata %>% 
  mutate(Date=as.factor(format.Date(faunadata$Date,"%Y"))) %>% 
  distinct(Site, Year, Point, Replicate) %>%
  group_by(Site, Year, Point) %>%
  mutate(Year = as.factor(Year)) %>% 
  summarise(count = n()) %>%
  ungroup() %>%
  mutate(State = factor(case_when(count==3 ~ "Good", count < 3 ~ "Sample(s) missing", count > 3 ~ "Too many samples")))

ggplot(samplespoint, aes(x=Year,y=Point,fill=State)) +
  geom_tile(alpha=0.5) +
  scale_fill_manual(values=c("palegreen3","lightblue", "indianred")) +
  geom_text(aes(label=count)) +
  theme(text=element_text(size =15), axis.text.x = element_text(angle = 90, size = 15), legend.position = "bottom")

Supporting Figure 1: Number of grab samples taken every year at each point. Empty cells show points where no samples were collected at a given date due to field constraints.

Habitat Complexity

Take a look at the structure of the final complexity dataset

tar_load(comp)
knitr::kable(head(comp))

 

First lines of the complexity dataset

Mini	Site	Point	Sample	Branching_density	L	I	S	Total_Density	Sphericity	DR1	DR2	DR3
MBE_1	Belle-ile	Belle-ile 1	1	5.8	12.85333	9.143333	8.303333	181696	0.8371338	0.6460062	0.8153846	0.7113589
MBE_1	Belle-ile	Belle-ile 1	2	1.0	16.07667	7.883333	5.393333	181696	0.6122605	0.3354758	0.7669267	0.4903587
MBE_1	Belle-ile	Belle-ile 1	3	3.0	15.30000	8.103333	7.190000	181696	0.7470808	0.4699346	0.8873818	0.5296296
MBE_1	Belle-ile	Belle-ile 1	4	4.8	12.91667	8.476667	6.203333	181696	0.7057078	0.4802581	0.6613704	0.6562581
MBE_1	Belle-ile	Belle-ile 1	5	4.2	24.54667	14.523333	8.970000	181696	0.6088477	0.3654264	0.6434838	0.5916621
MBE_1	Belle-ile	Belle-ile 1	6	3.0	16.35667	9.416667	6.783333	181696	0.6684949	0.4147137	0.7249304	0.5757082

 

Most variables are not normally distributed so we apply a box-cox (Box and Cox 1964) transformation and check correlations between the transformed variables

tar_read(pairscomp)

Correlogram of box-cox transformed HC variables

PCA of all observations

Let’s check it without removing highly correlated variables

tar_read(pcatot)

PCA of all observations and all variables

Let’s check the biplots of the selected variables

tar_read(pcasel)

Supporting Figure 2: PCA of all observations after variable selection

PCA of median values

Let’s check the biplots of the selected variables

tar_read(pcamed)

Supporting Figure 3: Principal Component Analysis (PCA) of (A) median values of standardized habitat complexity metrics. Scaling 1 (distance biplot) in left panel, with outer plots representing the distribution at each site, and scaling 2 (correlation biplot) in right panel. Circles represent the median values for each sampling point, while triangles represent each site centroid with their relative convex hulls. Branching density - density of apical branches per rhodolith. L, I, S - Larger, Intermediate and Smaller diameter of a rhodolith. DR3 and Sphericity - Diameter ratios used to describe maerl shape. Maerl density - density of rhodoliths at each point. Different levels of bed and rhodolith complexity are exemplified

Correlation between the centroids of the total PCA and the scores of the reduced PCA

tar_load(corcomp)
tar_load(rv)
plot(corcomp[[1]])
plot(corcomp[[2]])
tar_read(cortestcomp1)
tar_read(cortestcomp2)

    Pearson's product-moment correlation

data:  med_sel2$PC1_c and med_sel2$PC1_score
t = -16.404, df = 28, p-value = 6.824e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9769962 -0.9000432
sample estimates:
       cor 
-0.9517122 

    Pearson's product-moment correlation

data:  med_sel2$PC2_c and med_sel2$PC2_score
t = 25.364, df = 28, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9557115 0.9900324
sample estimates:
     cor 
0.978924 

(a) PC1

(b) PC2

Supporting Figure 4: Relation between Full PCA centroids and Reduced PCA scores

RV coefficient = 0.8744522, p-value = 6.8861509^{-12}

Macrofaunal diversity

# Total richness
tar_load(faunaclass)
total_rich <- nrow(faunaclass %>% 
  distinct(., Species)) #725 species

# Total richness by sediment position

epi_rich <- nrow(faunaclass %>% 
  filter(Position == "Epifauna") %>% 
  distinct(., Species)) #341 epifaunal species


inf_rich <- nrow(faunaclass %>% 
  filter(Position == "Infauna") %>% 
  distinct(., Species)) #266  infaunal species


int_rich <- nrow(faunaclass %>% 
  filter(Position == "Interstice") %>% 
  distinct(., Species)) #118 interstitial species

There are 725 species in total, 341 being epifaunal, 266 being infaunal, and 118 interstitial.

Taxa from 14 phyla were identified, and Annelida, Arthropoda, Mollusca and Echinodermata were dominant, although Chordata, Cnidaria and Nemertea were also abundant at some sites. Some beds were consistently dominated by polychaetes, while others were always dominated by crustaceans, and a few were more even over the years with dominance shifts between Annelida, Arthropoda and Mollusca (Figure S 5). Maerl annelid communities were dominated by the mobile predators eunicids (Eunicidae, Lumbrineridae, Dorvilleidae), the crawling surface deposit feeders spionids (Spionidae), the tube dwelling suspension feeding sabellids (Serpullidae), and the subsurface deposit feeding knot worms (Polygordidae; Figure S 6 - A). As for arthropods (Figure S 6 - B), the decapods were dominant in most beds, namely the filter-feeding porcelain crabs Pisidia longicornis (Linnaeus, 1767; Porcellanidae), which was consistently dominant. The subsurface deposit feeding Corophiidae amphipods and Apseudiidae tanaids, as well as the scavenging Lysianassoidea amphipods were also abundant in most sites. Finally, maerl mollusc communities (Figure S 6 - C) were mostly dominated by small grazing and deposit feeding gastropods (Cerithiidae, Rissoidea and Trochidae), by endo and epifaunal filter-feeding pectinid bivalves (namely Veneridae), and by chitons (Leptochitonidae).

tar_read(relab_phy)
tar_read(relab_ord) 

Supporting Figure 5: Average Relative Abundance of Main Phyla and Orders

(a) Main Phylla

(b) Main Orders

(p.s.: it is normal to have NAs for some orders as higher classification for some of the species is ambiguous or unknown.)

tar_read(relab_arth)
tar_read(relab_ann) 
tar_read(relab_mol)

Supporting Figure 6: Families Average Relative Abundance for Each Main Phyla

(a) Arthropoda

(b) Annelida

 

(c) Mollusca

 

Effects of HC on regional diversity patterns

Redundancy analysis

After variable selection, we add sites as a factor to understand if we describe them well with the environmental variables we chose. We can look at the first two axes to have an idea of the relationships between the different explanatory variables.

tar_load(rdacomp)
tar_load(rdahydro)
tar_load(rdagranulo)
tar_load(rdatemp)
tar_read(rdafselcomp)

          variables order         R2      R2Cum  AdjR2Cum        F pvalue
1        Sphericity     4 0.07252058 0.07252058 0.0698400 27.05410  0.001
2 Branching_density     1 0.05583365 0.12835423 0.1233012 22.09912  0.001
3     Total_Density     3 0.03274661 0.16110084 0.1537849 13.42811  0.001
4                 L     2 0.02417722 0.18527806 0.1757769 10.17867  0.001
5               DR3     5 0.01151774 0.19679579 0.1850530  4.90419  0.001

tar_read(rdafseltemp)

  variables order         R2      R2Cum   AdjR2Cum         F pvalue
1      T_sd     2 0.03456694 0.03456694 0.03177668 12.388392  0.001
2    T_mean     1 0.01675283 0.05131977 0.04582018  6.092385  0.001

tar_read(rdafselgranulo)

  variables order         R2     R2Cum   AdjR2Cum        F pvalue
1       Mud     1 0.08700660 0.0870066 0.08436789 32.97317  0.001
2    Gravel     2 0.04160464 0.1286112 0.12355971 16.47210  0.001
3        OM     3 0.01247367 0.1410849 0.13359437  4.99577  0.001

tar_read(rdafselhydro)

     variables order         R2      R2Cum   AdjR2Cum        F pvalue
1 Current_mean     1 0.06103081 0.06103081 0.05831702 22.48919  0.001
2    Fetch_max     2 0.04553573 0.10656654 0.10138721 17.58366  0.001

tar_load(rdasite)
tar_read(triplotrda)

RDA triplot showing the variability in species composition related to physical environmental variables and habitat complexity. The first two axes are shown.

Adjusted R²: 0.4279943. We check the model’s validity. Only 99 permutations were chosen for the validation by axis and by term here as they are really time consuming but the results are the same with 999 permutations. (Output is collapsed, click on code to see)

tar_read(aovsite)
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: rda(formula = bcdens ~ Mud + Depth + Current_mean + Branching_density + Fetch_max + T_sd + Sphericity + T_mean + DR3 + Gravel + Total_Density + L + OM + Year + Site, data = envcomp)
##           Df Variance      F Pr(>F)    
## Model     23  0.28507 12.289  0.001 ***
## Residual 324  0.32679                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tar_read(aovaxis)
## Permutation test for rda under reduced model
## Forward tests for axes
## Permutation: free
## Number of permutations: 99
## 
## Model: rda(formula = bcdens ~ Mud + Depth + Current_mean + Branching_density + Fetch_max + T_sd + Sphericity + T_mean + DR3 + Gravel + Total_Density + L + OM + Year + Site, data = envcomp)
##           Df Variance       F Pr(>F)   
## RDA1       1  0.07620 75.5463   0.01 **
## RDA2       1  0.04629 45.8898   0.01 **
## RDA3       1  0.04244 42.0730   0.01 **
## RDA4       1  0.02841 28.1713   0.01 **
## RDA5       1  0.01876 18.5971   0.01 **
## RDA6       1  0.01339 13.2736   0.01 **
## RDA7       1  0.01225 12.1454   0.01 **
## RDA8       1  0.00957  9.4930   0.01 **
## RDA9       1  0.00781  7.7480   0.01 **
## RDA10      1  0.00710  7.0381   0.01 **
## RDA11      1  0.00557  5.5256   0.01 **
## RDA12      1  0.00307  3.0402   0.01 **
## RDA13      1  0.00239  2.3719   0.02 * 
## RDA14      1  0.00202  2.0023   0.14   
## RDA15      1  0.00176  1.7401   0.38   
## RDA16      1  0.00154  1.5284   0.59   
## RDA17      1  0.00133  1.3138   0.82   
## RDA18      1  0.00113  1.1189   0.98   
## RDA19      1  0.00103  1.0169   1.00   
## RDA20      1  0.00088  0.8709   1.00   
## RDA21      1  0.00077  0.7609   1.00   
## RDA22      1  0.00073  0.7236   1.00   
## RDA23      1  0.00065  0.6482   1.00   
## Residual 324  0.32679                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tar_read(aovterm)
## Permutation test for rda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 99
## 
## Model: rda(formula = bcdens ~ Mud + Depth + Current_mean + Branching_density + Fetch_max + T_sd + Sphericity + T_mean + DR3 + Gravel + Total_Density + L + OM + Year + Site, data = envcomp)
##                    Df Variance       F Pr(>F)   
## Mud                 1  0.05324 52.7815   0.01 **
## Depth               1  0.03189 31.6154   0.01 **
## Current_mean        1  0.03207 31.7934   0.01 **
## Branching_density   1  0.02296 22.7680   0.01 **
## Fetch_max           1  0.01678 16.6339   0.01 **
## T_sd                1  0.01202 11.9166   0.01 **
## Sphericity          1  0.00898  8.9014   0.01 **
## T_mean              1  0.00709  7.0263   0.01 **
## DR3                 1  0.00610  6.0457   0.01 **
## Gravel              1  0.00858  8.5018   0.01 **
## Total_Density       1  0.00236  2.3400   0.01 **
## L                   1  0.00491  4.8632   0.01 **
## OM                  1  0.00426  4.2272   0.01 **
## Year                1  0.00573  5.6773   0.01 **
## Site                9  0.06813  7.5051   0.01 **
## Residual          324  0.32679                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Hierarchical partitioning

Figure 1  

tar_read(upsetmain)

Figure 1 (main text)

tar_read(upsetsep)

Supporting Figure 7: Hierarchical partitioning separating Rhodolith and Bed complexity

Effects of HC in local diversity

Figure 2  

knitr::include_graphics("figure2a.png", dpi = 90)
tar_read(psdens) #figure 2-b

Figure 2 (main text)

Observed richness

Check if the relationships between HC and species richness are the same for the total macrofauna as well as for the main phyla and the main compartments (different positions in sediment) separately. Main results shown in Figure 2

Total macrofauna

Now we first forward select environmental variables and then add sites as a random factor to control for pseudo-replication and other site-related variables that were not taken into account.

We check models with quadratic functions of both PC1 and PC2 to test for a unimodal relationship. Unimodal trends are never significant, suggesting an overall linear relationship. A significant interaction is found between PC1² and both PC2 and PC2² in the last model in Table S 1. Since there’s a very slight difference in AICs and BICs between the last two models, we keep the more parsimonious one.

tar_load(c(modfixrich1, modfixrichpoly1, modfixrich2, modfixrichpoly2, modfixrich12, modfixrichpoly12))

jtools::export_summs(
  modfixrich1, 
  modfixrichpoly1,
  modfixrich2,
  modfixrichpoly2,
  modfixrich12,
  modfixrichpoly12,
  model.names = c("S ~ PC1", "S ~ PC1+PC1^2", "S ~ PC2", "S ~ PC2+PC2^2", "S ~ PC1*PC2","S ~ (PC1+PC1^2)*(PC2+PC2^2)"),
  statistics = c(
    N = "nobs",
    DF = "df.residual",
    AIC = "AIC",
    BIC = "BIC",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
  coefs = c(
    "Rhodolith \n complexity \n (PC1)" = "PC1_score",
    "Rhodolith \n complexity \n (PC1)" = "`poly(PC1_score, 2)`1",
    "PC1^2" = "`poly(PC1_score, 2)`2",
    "Bed complexity (PC2)" = "PC2_score",
    "Bed complexity (PC2)" = "`poly(PC2_score, 2)`1",
    "PC2^2" = "`poly(PC2_score, 2)`2",
    "PC1:PC2" = "PC1_score:PC2_score",
    "PC1^2:PC2" ="`poly(PC1_score, 2)`2:`poly(PC2_score, 2)`1",
    "PC1:PC2^2" ="`poly(PC1_score, 2)`1:`poly(PC2_score, 2)`2",
    "PC1^2:PC2^2" ="`poly(PC1_score, 2)`2:`poly(PC2_score, 2)`2"
  ),
  scale = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 1: Model statistics of the linear and unimodal models of Richness

	S ~ PC1		S ~ PC1+PC1^2		S ~ PC2		S ~ PC2+PC2^2		S ~ PC1*PC2		S ~ (PC1+PC1^2)*(PC2+PC2^2)
Rhodolith complexity (PC1)	8.22 ***	(1.00)	8.21 ***	(1.00)					8.38 ***	(0.91)	6.96 ***	(1.02)
PC1^2			1.37	(1.00)							-1.25	(1.06)
Bed complexity (PC2)					8.14 ***	(1.00)	8.14 ***	(1.00)	8.03 ***	(0.92)	8.89 ***	(1.17)
PC2^2							0.44	(1.00)			1.30	(1.20)
PC1:PC2									-0.87	(0.97)
PC1^2:PC2											4.02 **	(1.25)
PC1:PC2^2											-1.80	(1.30)
PC1^2:PC2^2											-3.58 **	(1.19)
N	348		348		348		348		348		348
DF	346.00		345.00		346.00		345.00		344.00		339.00
AIC	3028.54		3028.65		3029.73		3031.53		2956.83		2945.82
BIC	3040.09		3044.06		3041.28		3046.94		2976.10		2984.35
Adjusted R2	0.16		0.16		0.16		0.16		0.32		0.35
F	67.23		34.64		65.82		32.93		55.56		24.45
p-value	0.00		0.00		0.00		0.00		0.00		0.00
All continuous predictors are mean-centered and scaled by 1 standard deviation. The outcome variable is in its original units. *** p < 0.001; ** p < 0.01; * p < 0.05.

tar_load(c(modfixrich12, modfrsel, modrrsel))

jtools::export_summs(
  modfixrich12,
  modfrsel,
  modrrsel,
  model.names = c("S ~ Complexity", "S ~ Complexity + Environment", "S ~ Complexity + Environment + (1|Site)"),
  statistics = c(
    N = "nobs",
    DF = "df.residual",
    AIC = "AIC",
    BIC = "BIC",
    `Marginal R2` = "r.squared.fixed",
    `Conditional R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
  coefs = c(
    "Rhodolith complexity (PC1)" = "PC1_score", 
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "PC1_score:PC2_score",
    "Depth",
    "Exposure" = "Fetch_max",
    "Mean bottom temperature" = "T_mean",
    "Organic Matter %" = "OM",
    "Year"
  ),
  scale = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 2: Model statistics of the final selected models of Richness

	S ~ Complexity		S ~ Complexity + Environment		S ~ Complexity + Environment + (1|Site)
Rhodolith complexity (PC1)	8.38 ***	(0.91)	2.89 *	(1.20)	2.08	(1.29)
Bed complexity (PC2)	8.03 ***	(0.92)	4.30 ***	(1.15)	4.05 **	(1.52)
PC1:PC2	-0.87	(0.97)	0.04	(0.91)	2.04	(1.08)
Depth			-5.33 ***	(1.25)	-3.06	(2.30)
Exposure			-2.75	(1.41)	-1.54	(2.70)
Mean bottom temperature			2.05 *	(0.88)	1.38	(0.86)
Organic Matter %			2.56 *	(1.12)	3.72 **	(1.21)
Year			1.71	(0.87)	1.78 *	(0.83)
N	348		348		348
DF	344.00		339.00		337.00
AIC	2956.83		2905.35		2872.96
BIC	2976.10		2943.88		2915.33
Marginal R2					0.29
Conditional R2	0.33		0.44		0.43
Adjusted R2	0.32		0.42
F	55.56		32.69
p-value	0.00		0.00
All continuous predictors are mean-centered and scaled by 1 standard deviation. The outcome variable is in its original units. *** p < 0.001; ** p < 0.01; * p < 0.05.

By phylum

tar_load(c(modrrann, modrrart, modrrmol, modrrepi, modrrinf, modrrint))

jtools::export_summs(
  modrrann,
  modrrart,
  modrrmol,
  model.names = c("Annelida", "Arthropoda", "Mollusca"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `Marginal R2` = "r.squared.fixed",
    `Conditional R2` = "r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
  coefs = c(
    "Rhodolith complexity (PC1)" = "PC1_score", 
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "PC1_score:PC2_score"
  ),
  scale = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 3: Model statistics of richness as a function of HC for each main phyla

	Annelida		Arthropoda		Mollusca
Rhodolith complexity (PC1)	1.50 *	(0.65)	0.43	(0.43)	-0.15	(0.47)
Bed complexity (PC2)	2.50 **	(0.79)	0.90	(0.53)	0.11	(0.59)
PC1:PC2	1.50 **	(0.56)	0.59	(0.36)	0.06	(0.40)
N	348		348		348
Residual DF	342.00		342.00		342.00
Marginal R2	0.11		0.02		0.00
Conditional R2	0.38		0.52		0.51
p-value
All continuous predictors are mean-centered and scaled by 1 standard deviation. The outcome variable is in its original units. *** p < 0.001; ** p < 0.01; * p < 0.05.

By compartment

jtools::export_summs(
  modrrepi,
  modrrinf,
  modrrint,
  model.names = c("Epifauna", "Infauna", "Interstitial fauna"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `Marginal R2` = "r.squared.fixed",
    `Conditional R2` = "r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
    coefs = c(
    "Rhodolith complexity (PC1)" = "PC1_score", 
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "PC1_score:PC2_score"
  ),
  scale = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 4: Model statistics of richness as a function of HC for each compartiment

	Epifauna		Infauna		Interstitial fauna
Rhodolith complexity (PC1)	0.78	(0.74)	0.37	(0.55)	0.80 **	(0.28)
Bed complexity (PC2)	1.47	(0.91)	1.35 *	(0.68)	1.35 ***	(0.33)
PC1:PC2	1.40 *	(0.62)	0.76	(0.47)	0.36	(0.25)
N	348		348		347
Residual DF	342.00		342.00		341.00
Marginal R2	0.03		0.03		0.15
Conditional R2	0.52		0.44		0.31
p-value
All continuous predictors are mean-centered and scaled by 1 standard deviation. The outcome variable is in its original units. *** p < 0.001; ** p < 0.01; * p < 0.05.

As we can see, the marginal R2 for the models containing only a subset of the macrofauna are quite low. However, although the effects of HC are less evident in these cases, the pattern for most subsets is very similar to the general pattern, with very slight positive effect of rhodolith complexity and a more important positive effect of bed complexity. Nevertheless, rhodolith complexity seems to play a more important role in driving annelid richness than the other main phyla.

Density of macrofauna

Total macrofauna

tar_load(c(modfixdens12, modfdsel, modrdsel))
jtools::export_summs(
  modfixdens12,
  modfdsel,
  modrdsel,
  model.names = c("Density ~ Complexity", "Density ~ Complexity + Environment", "Density ~ Complexity + Environment + (1|Site)"),
  statistics = c(
    N = "nobs",
    DF = "df.residual",
    AIC = "AIC",
    BIC = "BIC",
    `Marginal R2` = "r.squared.fixed",
    `Conditional R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
    coefs = c(
    "Rhodolith complexity (PC1)" = "PC1_score", 
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "PC1_score:PC2_score",
    "Mean current velocity" = "Current_mean",
    "Depth",
    "Exposure" = "Fetch_max",
    "Gravel",
    "Organic Matter %" = "OM",
    "Mean Temperature" = "T_mean",
    "Year"
  ),
  scale = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 5: Model statistics of macrofaunal density as a function of HC

	Density ~ Complexity		Density ~ Complexity + Environment		Density ~ Complexity + Environment + (1|Site)
Rhodolith complexity (PC1)	0.42 ***	(0.05)	0.21 ***	(0.06)	0.06	(0.05)
Bed complexity (PC2)	0.09	(0.05)	-0.07	(0.05)	0.13 *	(0.07)
PC1:PC2	-0.31 ***	(0.05)	-0.14 **	(0.05)	0.04	(0.04)
Mean current velocity			-0.27 ***	(0.05)	-0.50 *	(0.23)
Depth			-0.29 ***	(0.06)	0.08	(0.13)
Exposure			0.20 **	(0.07)	0.18	(0.17)
Gravel			0.24 ***	(0.04)	0.05	(0.05)
Organic Matter %			0.20 ***	(0.05)	0.14 **	(0.05)
Mean Temperature			0.11 **	(0.04)	0.06	(0.03)
Year			0.03	(0.04)	0.05	(0.03)
N	348		348		348
DF	344.00		337.00		335.00
AIC	889.58		767.68		687.22
BIC	908.84		813.91		737.30
Marginal R2					0.24
Conditional R2	0.26		0.50		0.69
Adjusted R2	0.25		0.48
F	39.42		33.22
p-value	0.00		0.00
All continuous predictors are mean-centered and scaled by 1 standard deviation. The outcome variable is in its original units. *** p < 0.001; ** p < 0.01; * p < 0.05.

By phylum

tar_load(c(modfdann, modrdann, modfdart, modrdart, modfdmol, modrdmol, modfdepi, modrdepi, modfdinf, modrdinf, modfdint, modrdint))

jtools::export_summs(
  modfdann,
  modrdann,
  modfdart,
  modrdart,
  modfdmol,
  modrdmol,
  model.names = c("Annelida LM","Annelida LMM", "Arthropoda LM", "Arthropoda LMM", "Mollusca LM", "Mollusca LMM"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `Marginal R2` = "r.squared.fixed",
    `Conditional R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
   coefs = c(
    "Rhodolith complexity (PC1)" = "PC1_score", 
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "PC1_score:PC2_score",
    "Mean current velocity" = "Current_mean",
    "Depth",
    "Exposure" = "Fetch_max",
    "Gravel",
    "Organic Matter %" = "OM",
    "Mean Temperature" = "T_mean",
    "Year"
  ),
  scale = TRUE,
  transform.response = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 6: Model statistics of Density as a function of HC for each main phyla

	Annelida LM		Annelida LMM		Arthropoda LM		Arthropoda LMM		Mollusca LM		Mollusca LMM
Rhodolith complexity (PC1)	0.05	(0.05)	0.07	(0.05)	0.31 ***	(0.07)	0.03	(0.05)	0.02	(0.05)	-0.02	(0.06)
Bed complexity (PC2)	0.11 *	(0.05)	0.11	(0.06)	0.00	(0.06)	0.10	(0.06)	0.31 ***	(0.05)	0.18 **	(0.07)
PC1:PC2	-0.01	(0.04)	0.05	(0.05)	-0.18 ***	(0.05)	0.06	(0.04)	-0.05	(0.04)	-0.01	(0.05)
Mean current velocity					-0.29 ***	(0.06)	-0.10	(0.21)
Depth	-0.31 ***	(0.05)	-0.21 *	(0.10)					-0.42 ***	(0.06)	-0.36 **	(0.09)
Exposure	-0.29 ***	(0.06)	-0.37 **	(0.12)	0.30 ***	(0.07)	0.31	(0.17)
Gravel					0.31 ***	(0.05)	0.02	(0.04)	0.17 ***	(0.04)	0.05	(0.05)
Organic Matter %	0.06	(0.05)			0.22 ***	(0.06)	0.10 *	(0.05)	0.09 *	(0.05)	0.12 *	(0.05)
Mean Temperature	0.09 *	(0.04)	0.07 *	(0.04)					0.10 **	(0.04)	0.09 *	(0.04)
Year	0.15 ***	(0.04)	0.16 ***	(0.03)	0.06	(0.04)	0.07 *	(0.03)	-0.06	(0.04)	-0.06	(0.04)
N	348		348		348		348		348		348
Residual DF	338.00		338.00		339.00		337.00		339.00		337.00
Marginal R2			0.47				0.04				0.35
Conditional R2	0.59		0.58		0.37		0.75		0.54		0.47
Adjusted R2	0.58				0.36				0.53
F	53.91				24.94				49.88
p-value	0.00				0.00				0.00
All continuous variables are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

# tar_load(surfann)
# save_image(surfann, "surfann.pdf", width = 1080, height = 1080)
knitr::include_graphics("surfann.pdf")
# tar_load(surfart)
# save_image(surfart, "surfdart.pdf", width = 1080, height = 1080)
knitr::include_graphics("surfart.pdf")
# tar_read(surfmol)
# save_image(surfmol, "surfmol.pdf", width = 1080, height = 1080)
knitr::include_graphics("surfmol.pdf")

Error in knitr::include_graphics("surfart.pdf"): Cannot find the file(s): "surfart.pdf"

Error in knitr::include_graphics("surfmol.pdf"): Cannot find the file(s): "surfmol.pdf"

Marginal predictions of main phyla density as a function of HC

By compartment

jtools::export_summs(
  modfdepi,
  modrdepi,
  modfdinf,
  modrdinf,
  modfdint,
  modrdint,
  model.names = c("Epifauna LM", "Epifauna LMM", "Infauna LM", "Infauna LMM", "Interstitial fauna LM", "Interstitial fauna LMM"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `Marginal R2` = "r.squared.fixed",
    `Conditional R2` = "r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
   coefs = c(
    "Rhodolith complexity (PC1)" = "PC1_score", 
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "PC1_score:PC2_score",
    "Mean current velocity" = "Current_mean",
    "Depth",
    "Exposure" = "Fetch_max",
    "Gravel",
    "Organic Matter %" = "OM",
    "Mud",
    "Mean Temperature" = "T_mean",
    "Temperature Variation (sd)" = "T_sd",
    "Year"
  ),
  scale = TRUE,
  transform.response = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 7: Model statistics of density of macrofauna as a function of HC for each compartiment

	Epifauna LM		Epifauna LMM		Infauna LM		Infauna LMM		Interstitial fauna LM		Interstitial fauna LMM
Rhodolith complexity (PC1)	0.31 ***	(0.06)	0.07	(0.05)	0.10	(0.05)	0.01	(0.06)	0.06	(0.05)	0.04	(0.05)
Bed complexity (PC2)	0.01	(0.05)	0.11	(0.07)	0.17 ***	(0.05)	0.14	(0.07)	0.22 ***	(0.04)	0.20 **	(0.06)
PC1:PC2	-0.09	(0.05)	0.07	(0.04)	-0.11 *	(0.05)	-0.00	(0.05)	-0.09 *	(0.04)	-0.03	(0.04)
Mean current velocity	-0.28 ***	(0.06)	-0.23	(0.20)
Depth					-0.48 ***	(0.06)	-0.20	(0.11)
Exposure	0.27 ***	(0.07)	0.21	(0.17)					-0.34 ***	(0.06)	-0.40 ***	(0.09)
Gravel	0.38 ***	(0.05)	0.07	(0.05)	-0.25 ***	(0.05)	-0.10	(0.05)	0.26 ***	(0.04)	0.19 ***	(0.05)
Organic Matter %	0.27 ***	(0.05)	0.16 **	(0.05)					0.03	(0.05)	0.04	(0.05)
Mud									0.33 ***	(0.05)	0.25 ***	(0.06)
Mean Temperature	0.08	(0.04)	0.04	(0.03)	0.08	(0.04)	0.05	(0.04)
Temperature Variation (sd)					-0.01	(0.04)	-0.01	(0.06)
Year	0.00	(0.04)	0.02	(0.03)	0.09 *	(0.04)	0.09 *	(0.04)	0.14 ***	(0.03)	0.14 ***	(0.03)
N	348		348		348		348		348		348
Residual DF	338.00		336.00		339.00		337.00		339.00		337.00
Marginal R2			0.09				0.11				0.56
Conditional R2	0.42		0.66		0.43		0.49		0.60		0.62
F	27.06				32.32				64.41
p-value	0.00				0.00				0.00
All continuous variables are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

tar_read(psdtrait)

Macrofaunal densities as a function of HC for each faunal compartment

N2

tar_read(psN2)

Supporting Figure 8: Regression coefficients of LMs and LMM of Simpson’s inverse diversity (N2) as a function of HC and physical environmental variables.

J

tar_read(psJ)

Supporting Figure 9: Regression coefficients of LMs and LMM of Pielou’s evenness (J) as a function of HC and physical

Community stability

Figure 3  

knitr::include_graphics("figure3a.png")

tar_read(bdtrait)
tar_read(bdphyl)

Effects of HC on community stability by compartiment

Effects of HC on community stability by phylum - not shown in main text

tar_read(replplot)

Supporting Figure 10: Relative importance of the Replacement component of temporal BD

Effects of HC on community stability

Total Macrofauna

tar_load(c(modbdtot, modrdftot, modrptot))
jtools::export_summs(
  modbdtot,
  modrptot,
  modrdftot,
  model.names = c("BDtotal", "Replacement", "Richness Difference"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
   coefs = c(
    "Rhodolith complexity (PC1)" = "`poly(PC1_score, 2)`1",
    "Rhodolith complexity (PC1)^2" = "`poly(PC1_score, 2)`2",
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "`poly(PC1_score, 2)`1:PC2_score",
    "PC1ˆ2:PC2" = "`poly(PC1_score, 2)`2:PC2_score"
  ),
  scale = TRUE,
  transform.response = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 8: Models statistics of temporal diversity metrics as a function of HC

	BDtotal		Replacement		Richness Difference
Rhodolith complexity (PC1)	-0.51 **	(0.16)	-0.42 *	(0.17)	-0.29	(0.18)
Rhodolith complexity (PC1)^2	-0.23	(0.16)	-0.10	(0.17)	-0.27	(0.19)
Bed complexity (PC2)	-0.18	(0.17)	-0.21	(0.19)	-0.02	(0.20)
PC1:PC2	0.28	(0.19)	0.11	(0.21)	0.32	(0.22)
PC1ˆ2:PC2	-0.07	(0.20)	-0.27	(0.22)	0.24	(0.24)
N	30		30		30
Residual DF	24.00		24.00		24.00
R2	0.43		0.32		0.20
Adjusted R2	0.31		0.18		0.03
F	3.55		2.23		1.20
p-value	0.02		0.08		0.34
All continuous variables are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

By phylum

Annelida

tar_load(c(modbdann, modrdfann, modrpann))
jtools::export_summs(
  modbdann,
  modrpann,
  modrdfann,
  model.names = c("BDtotal", "Replacement", "Richness Difference"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
   coefs = c(
    "Rhodolith complexity (PC1)" = "`poly(PC1_score, 2)`1",
    "Rhodolith complexity (PC1)^2" = "`poly(PC1_score, 2)`2",
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "`poly(PC1_score, 2)`1:PC2_score",
    "PC1ˆ2:PC2" = "`poly(PC1_score, 2)`2:PC2_score"
  ),
  scale = TRUE,
  transform.response = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 9: Models statistics of annelid temporal diversity metrics as a function of HC

	BDtotal		Replacement		Richness Difference
Rhodolith complexity (PC1)	-0.61 ***	(0.14)	-0.64 ***	(0.15)	-0.16	(0.19)
Rhodolith complexity (PC1)^2	-0.27	(0.15)	-0.11	(0.15)	-0.30	(0.19)
Bed complexity (PC2)	-0.08	(0.16)	0.01	(0.16)	-0.14	(0.20)
PC1:PC2	0.22	(0.17)	0.10	(0.18)	0.22	(0.23)
PC1ˆ2:PC2	-0.10	(0.19)	-0.22	(0.19)	0.12	(0.24)
N	30		30		30
Residual DF	24.00		24.00		24.00
R2	0.51		0.49		0.18
Adjusted R2	0.41		0.38		0.01
F	4.98		4.62		1.03
p-value	0.00		0.00		0.42
All continuous variables are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

Arthropoda

tar_load(c(modbdart, modrdfart, modrpart))
jtools::export_summs(
  modbdart,
  modrpart,
  modrdfart,
  model.names = c("BDtotal", "Replacement", "Richness Difference"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
   coefs = c(
    "Rhodolith complexity (PC1)" = "`poly(PC1_score, 2)`1",
    "Rhodolith complexity (PC1)^2" = "`poly(PC1_score, 2)`2",
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "`poly(PC1_score, 2)`1:PC2_score",
    "PC1ˆ2:PC2" = "`poly(PC1_score, 2)`2:PC2_score"
  ),
  scale = TRUE,
  transform.response = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 10: Models statistics of arthropod temporal diversity metrics as a function of HC

	BDtotal		Replacement		Richness Difference
Rhodolith complexity (PC1)	-0.42 *	(0.17)	-0.29	(0.16)	-0.38 *	(0.18)
Rhodolith complexity (PC1)^2	-0.09	(0.18)	-0.20	(0.16)	0.03	(0.18)
Bed complexity (PC2)	-0.23	(0.19)	-0.40 *	(0.18)	-0.02	(0.20)
PC1:PC2	0.22	(0.21)	-0.13	(0.19)	0.41	(0.22)
PC1ˆ2:PC2	-0.11	(0.22)	-0.41	(0.21)	0.16	(0.23)
N	30		30		30
Residual DF	24.00		24.00		24.00
R2	0.31		0.39		0.25
Adjusted R2	0.16		0.27		0.09
F	2.14		3.11		1.57
p-value	0.10		0.03		0.21
All continuous variables are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

Mollusca

tar_load(c(modbdmol, modrdfmol, modrpmol))
jtools::export_summs(
  modbdmol,
  modrpmol,
  modrdfmol,
  model.names = c("BDtotal", "Replacement", "Richness Difference"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
   coefs = c(
    "Rhodolith complexity (PC1)" = "`poly(PC1_score, 2)`1",
    "Rhodolith complexity (PC1)^2" = "`poly(PC1_score, 2)`2",
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "`poly(PC1_score, 2)`1:PC2_score",
    "PC1ˆ2:PC2" = "`poly(PC1_score, 2)`2:PC2_score"
  ),
  scale = TRUE,
  transform.response = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 11: Models statistics of mollusc temporal diversity metrics as a function of HC

	BDtotal		Replacement		Richness Difference
Rhodolith complexity (PC1)	-0.22	(0.16)	-0.06	(0.19)	-0.24	(0.18)
Rhodolith complexity (PC1)^2	-0.32	(0.16)	-0.16	(0.20)	-0.23	(0.19)
Bed complexity (PC2)	-0.35	(0.17)	-0.22	(0.21)	-0.20	(0.20)
PC1:PC2	0.30	(0.19)	0.19	(0.23)	0.15	(0.22)
PC1ˆ2:PC2	0.15	(0.21)	0.02	(0.25)	0.20	(0.24)
N	30		30		30
Residual DF	24.00		24.00		24.00
R2	0.40		0.13		0.19
Adjusted R2	0.27		-0.05		0.03
F	3.18		0.72		1.16
p-value	0.02		0.62		0.36
All continuous variables are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

By compartment

Epifauna

tar_load(c(modbdepi, modrdfepi, modrpepi))
jtools::export_summs(
  modbdepi,
  modrpepi,
  modrdfepi,
  model.names = c("BDtotal", "Replacement", "Richness Difference"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
   coefs = c(
    "Rhodolith complexity (PC1)" = "`poly(PC1_score, 2)`1",
    "Rhodolith complexity (PC1)^2" = "`poly(PC1_score, 2)`2",
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "`poly(PC1_score, 2)`1:PC2_score",
    "PC1ˆ2:PC2" = "`poly(PC1_score, 2)`2:PC2_score"
  ),
  scale = TRUE,
  transform.response = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 12: Models statistics of epifaunal temporal diversity metrics as a function of HC

	BDtotal		Replacement		Richness Difference
Rhodolith complexity (PC1)	-0.42 *	(0.18)	-0.25	(0.19)	-0.38 *	(0.18)
Rhodolith complexity (PC1)^2	-0.16	(0.18)	-0.06	(0.19)	-0.19	(0.19)
Bed complexity (PC2)	-0.04	(0.19)	-0.07	(0.21)	0.02	(0.20)
PC1:PC2	0.29	(0.21)	0.08	(0.23)	0.36	(0.22)
PC1ˆ2:PC2	-0.05	(0.23)	-0.26	(0.25)	0.18	(0.24)
N	30		30		30
Residual DF	24.00		24.00		24.00
R2	0.27		0.15		0.22
Adjusted R2	0.12		-0.03		0.06
F	1.82		0.82		1.38
p-value	0.15		0.55		0.27
All continuous variables are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

Infauna

tar_load(c(modbdinf, modrdfinf, modrpinf))
jtools::export_summs(
  modbdinf,
  modrpinf,
  modrdfinf,
  model.names = c("BDtotal", "Replacement", "Richness Difference"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
   coefs = c(
    "Rhodolith complexity (PC1)" = "`poly(PC1_score, 2)`1",
    "Rhodolith complexity (PC1)^2" = "`poly(PC1_score, 2)`2",
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "`poly(PC1_score, 2)`1:PC2_score",
    "PC1ˆ2:PC2" = "`poly(PC1_score, 2)`2:PC2_score"
  ),
  scale = TRUE,
  transform.response = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 13: Models statistics of infaunal temporal diversity metrics as a function of HC

	BDtotal		Replacement		Richness Difference
Rhodolith complexity (PC1)	-0.56 ***	(0.14)	-0.43 *	(0.17)	-0.28	(0.18)
Rhodolith complexity (PC1)^2	-0.35 *	(0.14)	-0.25	(0.17)	-0.20	(0.19)
Bed complexity (PC2)	-0.26	(0.15)	-0.16	(0.18)	-0.19	(0.20)
PC1:PC2	0.18	(0.16)	0.09	(0.20)	0.17	(0.22)
PC1ˆ2:PC2	-0.09	(0.18)	-0.18	(0.22)	0.13	(0.24)
N	30		30		30
Residual DF	24.00		24.00		24.00
R2	0.57		0.33		0.19
Adjusted R2	0.48		0.20		0.03
F	6.30		2.41		1.15
p-value	0.00		0.07		0.36
All continuous variables are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

Interstitial fauna

tar_load(c(modbdint, modrdfint, modrpint))
jtools::export_summs(
  modbdint,
  modrpint,
  modrdfint,
  model.names = c("BDtotal", "Replacement", "Richness Difference"),
  statistics = c(
    N = "nobs",
    `Residual DF` = "df.residual",
    `R2` = "r.squared",
    `Adjusted R2` = "adj.r.squared",
    `F` = "statistic",
    `p-value` = "p.value"
  ),
   coefs = c(
    "Rhodolith complexity (PC1)" = "`poly(PC1_score, 2)`1",
    "Rhodolith complexity (PC1)^2" = "`poly(PC1_score, 2)`2",
    "Bed complexity (PC2)" = "PC2_score",
    "PC1:PC2" = "`poly(PC1_score, 2)`1:PC2_score",
    "PC1ˆ2:PC2" = "`poly(PC1_score, 2)`2:PC2_score"
  ),
  scale = TRUE,
  transform.response = TRUE,
  error_pos = "right",
  bold_signif = .05
)

Supporting Table 14: Models statistics of interstitial fauna temporal diversity metrics as a function of HC

	BDtotal		Replacement		Richness Difference
Rhodolith complexity (PC1)	-0.33	(0.16)	-0.46 *	(0.17)	0.12	(0.18)
Rhodolith complexity (PC1)^2	-0.12	(0.17)	0.17	(0.17)	-0.31	(0.18)
Bed complexity (PC2)	-0.40 *	(0.18)	-0.26	(0.18)	-0.16	(0.20)
PC1:PC2	0.26	(0.20)	0.01	(0.20)	0.27	(0.22)
PC1ˆ2:PC2	-0.02	(0.21)	-0.32	(0.22)	0.30	(0.23)
N	30		30		30
Residual DF	24.00		24.00		24.00
R2	0.38		0.35		0.25
Adjusted R2	0.25		0.21		0.09
F	2.92		2.55		1.57
p-value	0.03		0.05		0.21
All continuous variables are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

References

Box, G. E. P., and D. R. Cox. 1964. “An Analysis of Transformations.” Journal of the Royal Statistical Society. Series B (Methodological) 26 (2): 211–52. https://www.jstor.org/stable/2984418.

Casajus N. 2022. Rcompendium: An r Package to Create a Package or Research Compendium Structure. https://github.com/FRBCesab/rcompendium.

Landau, William Michael. 2021. “The Targets r Package: A Dynamic Make-Like Function-Oriented Pipeline Toolkit for Reproducibility and High-Performance Computing.” Journal of Open Source Software 6 (57): 2959. https://doi.org/10.21105/joss.02959.